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概要と使いどころ
Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.
日本語の概要は準備中です。原文の説明を表示しています。
Professional network reconnaissance and port scanning using nmap. Supports various scan types (quick, full, UDP, stealth), service detection, vulnerability scanning, and NSE scripts. Use when you need to enumerate network services, detect versions, or perform network reconnaissance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when detecting and running project build systems automatically. Supports npm/yarn/pnpm/pip/poetry/gradle/maven/cargo/go/make. Triggers on build, test run, project setup, package manager detection.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.
日本語の概要は準備中です。原文の説明を表示しています。
Implement cheat codes, hidden interactions, and unlockable meta-content via time-windowed input sequence buffers, spam thresholds, look-at (dot-product) detectors, progress-% unlocks, and meta persistence in user://secrets.cfg. Use when adding Konami-style codes, curiosity spam eggs, glimmer cues, lockout anti-brute-force, or gallery flags that must survive slot deletes. Keywords: Konami, sequence buffer, meta secrets.cfg, visibility detector, lockout, glimmer, spam threshold, progress unlock.
日本語の概要は準備中です。原文の説明を表示しています。
Expert patterns for porting desktop games to mobile including touch control schemes (virtual joystick, gesture detection), UI scaling for small screens, performance optimization for mobile GPUs, battery life management, and platform-specific features. Use when creating mobile ports or cross-platform mobile builds. Trigger keywords: TouchScreenButton, virtual_joystick, gesture_detector, InputEventScreenTouch, InputEventScreenDrag, mobile_optimization, battery_saving, adaptive_performance, MOBILE_ENABLED.
日本語の概要は準備中です。原文の説明を表示しています。
Implement cheat codes, hidden interactions, and unlockable meta-content via time-windowed input sequence buffers, spam thresholds, look-at (dot-product) detectors, progress-% unlocks, and meta persistence in user://secrets.cfg. Use when adding Konami-style codes, curiosity spam eggs, glimmer cues, lockout anti-brute-force, or gallery flags that must survive slot deletes. Keywords: Konami, sequence buffer, meta secrets.cfg, visibility detector, lockout, glimmer, spam threshold, progress unlock.
日本語の概要は準備中です。原文の説明を表示しています。
Expert patterns for porting desktop games to mobile including touch control schemes (virtual joystick, gesture detection), UI scaling for small screens, performance optimization for mobile GPUs, battery life management, and platform-specific features. Use when creating mobile ports or cross-platform mobile builds. Trigger keywords: TouchScreenButton, virtual_joystick, gesture_detector, InputEventScreenTouch, InputEventScreenDrag, mobile_optimization, battery_saving, adaptive_performance, MOBILE_ENABLED.
日本語の概要は準備中です。原文の説明を表示しています。
Generates lapsed user detection and re-engagement screens with personalized return experiences, win-back offers, and inactivity tracking. Use when user wants to re-engage inactive users, detect lapsed users, or build return flows.
日本語の概要は準備中です。原文の説明を表示しています。
This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and qua
日本語の概要は準備中です。原文の説明を表示しています。
cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat
日本語の概要は準備中です。原文の説明を表示しています。
Expert knowledge for Azure AI Personalizer development including troubleshooting, decision making, security, configuration, and integrations & coding patterns. Use when choosing single vs multi-slot setups, tuning policies/exploration, using apprentice mode, explainability, or local inference, and other Azure AI Personalizer related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure Machine Learning (use azure-machine-learning), Azure AI Metrics Advisor (use azure-metrics-advisor), Azure AI Anomaly Detector (use azure-anomaly-detector).
日本語の概要は準備中です。原文の説明を表示しています。
Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.
日本語の概要は準備中です。原文の説明を表示しています。
Brand monitoring and mention tracking via the Brand.dev API. Use when asked to monitor brand mentions, track sentiment, find PR opportunities, detect logo usage, or analyze brand presence online. Trigger phrases: "brand monitoring", "mention tracking", "brand sentiment", "PR opportunities", "logo detection", "brand.dev", "brand mentions", "media monitoring".
日本語の概要は準備中です。原文の説明を表示しています。
Use for blue-team threat hunting, detection engineering with Sigma/YARA, SIEM query design, and incident detection validation.
日本語の概要は準備中です。原文の説明を表示しています。
Build and validate .arch/index.json — a committed map from each architectural component of this repository to the real directories and files that implement it, pinned to a git commit, with every path verified to exist. Also detects structural drift between that index and the current code. Use when asked to map a codebase, produce a component or code-ownership index, find which files implement a component, check whether architecture docs are stale, or detect architecture drift after a refactor. Triggers include 'map the codebase', 'component index', 'which files implement X', 'is the architecture still accurate', 'architecture drift', 'refresh the architecture index'.
日本語の概要は準備中です。原文の説明を表示しています。
Documentation collapse anti-patterns - detection rules, bad examples, and remediation strategies for type-mixing violations
日本語の概要は準備中です。原文の説明を表示しています。
Best practices for multi-language handling, locale routing, and detection strategies across App and Pages Router. Use when adding i18n, locale routing, or language detection in Next.js.
日本語の概要は準備中です。原文の説明を表示しています。
Validates defensive controls with paired authorized action and observation evidence, distinguishing blocked, prevented, detected, responded, and telemetry-gap outcomes. Use for purple-team validation design or offline fixture review; not unsanctioned testing or production efficacy claims.
日本語の概要は準備中です。原文の説明を表示しています。
Build standalone Angular components with Signals inputs, OnPush change detection, Control Flow, and Smart/Dumb patterns. Use when building standalone Angular components, implementing @if/@for control flow, applying OnPush change detection, or implementing Signals in Angular components.
日本語の概要は準備中です。原文の説明を表示しています。
统计与机器学习异常值检测工具。读取结构化表格数据，使用 IQR、Z-score、IsolationForest 或分组标准差一致性方法识别异常值，输出带异常标记、删除异常行或裁剪异常值后的结果文件。 当用户提到异常值检测、离群值识别、IQR、Z-score、IsolationForest、按实验组/分类组检查数值一致性等需求时使用此skill。 即使用户没有明确说出“statistical_outlier_detector”，只要任务涉及结构化数值字段异常识别或分组均值标准差一致性检查，就应该使用此skill。 不负责字段固定阈值上下限校验、枚举值校验、时间序列缺失/频率校验或格式转换。
日本語の概要は準備中です。原文の説明を表示しています。
Creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. Use when writing Semgrep rules or building custom static analysis detections.
日本語の概要は準備中です。原文の説明を表示しています。
Typosquatting detection, install script analysis, dependency confusion prevention, and phantom dependency detection for npm/pip.
日本語の概要は準備中です。原文の説明を表示しています。
30+ service-specific secret detection regex patterns, entropy-based detection, PEM/JWT/Base64 identification, and false positive filtering.
日本語の概要は準備中です。原文の説明を表示しています。